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Table 1: Used particle models and SiC material data Particle-Model Parameter Value Drag Force Drag Coefficient Schiller-Naumann Shear Lift Force Shear Lift Coefficient Method Sommerfeld Spin Lift Force Particle Lift Coefficient Method Sommerfeld Particle-Particle-Interaction Hertz Mindlin - - Rolling Resistance Rolling Resistance Method Force Proportional Linear Cohesion Work of Cohesion 0; 1 N/m Particle-Wall-Interaction Hertz Mindlin - - Rolling Resistance Rolling Resistance Method Force Proportional SiC material data E-Module: E = 400 GPa Density: ρ = 3210 kg/m³ Poisson‘s ratio ν = 0.33 For discretization of the model a polyhedral mesh with a base size of 2 mm and a particle size of 0.7 mm was used to reduce large computing times caused due to the huge number of particles.
In order to further reduce computing times, a reduction of E-Module is assumed.
Size reduction of the DEM particles would deliver a better spatial discretization at the cost of computation time and might minimize this effect.
An increase in forming speed by 61.5 % and therefore reduction in process time, reduces the needed time for simulation by 35 %.

The flow curves determined in this test were used as input data for the program code in MATLAB.
Complex geometric conditions arise from continuously increasing the reduction in wedge rolling, which lead to variable lengths of contact arc and of the strain rate among the samples.
Test on the deformation behaviour (Fig. 4) and the quantitative microstructure analysis (Fig. 8) showed that after a rolling reduction of about 30% the open porosity was completely eliminated in all composites.
The comparison of the process maps and the experimental data of all MMC compositions in Fig. 3 shows their dependence on the different rolling conditions.
A complete densification took place at a pass reduction of more than 30%; 2.

The process softens and swells the rubber resulting in the reduction of its viscosity by shortening the polymer chain by mechanical shear and chemical action.
In order to ensure that the proposed equations can predict the weight fraction of fillers that is relevant to the target thermal conductivity, a comparison of the results acquired from the proposed equations and experimental data was performed.
Fig. 3. demonstrates a comparison between the model prediction and the experimental results, and the results indicated that the Maxwell equation agrees with the experimental data.
The reduction of thermal expansion depends upon the increase in AlN loading.
For the period of continuously temperature sweeping, the storage modulus of the produced composites significantly decreased that active the reduction of energy absorption.

The EMG-data was afterwards processed and normalized using a standardized procedure in biomechanical analyses [14].
Comparing the EMG-data for both tasks with the MVC, only during the second task (outstretched arm), a noticeable reduction in muscle innervation occurred for the M. deltoideus (15.68 ± 0.64 [% MVC] with support, 27.08 ± 2.05 [% MVC] without support).
During the first task, no reduction occurred for the M. biceps brachii (9.10 ± 0.26 [% MVC] with support, 7.43 ± 0.84 [% MVC] without support).
The reasons for the differences in reduction while wearing the support system might be influenced by a) the different placement of the two support systems in conjunction to the joints and b) differences in grasping the object with a bend or outstretched arm, which could have had influences on the innervation of the biceps.

The integration of the substeps solution annealing and quenching in the extrusion process results in a marked reduction of the complete process chain.
The quenching by gas or two phase spray quenching allows the reduction of residual stresses and distortion due to the possibility of controlling the local surface heat transfer [2,3,13].
Fig. 3: Dependence of the heat transfer coefficient related to the radius r of the spray cone; measuring data depicted by points according to [8,9]; lines approximated using equation 1 (R = 30 mm, n = 0.9, k = 0.55) 0 5000 10000 15000 0 200 400 600 temperature [°C] heat transfer coefficient [W/m2 K] 3W 3A 3W 6A 6W 6A W: water pressure [bar] A: air pressure [bar] 0 4000 8000 12000 50 150 250 350 heat transfer coefficient [W/m2 K] temperature [°C] r = 20 mm r = 10 mm spray centreModelling of Gas and Spray Quenching Material properties.
Using the finite element method these can be predicted though there still is a lack of materials data suited for numerical simulations.

The signal obtained from IR camera is thermogram sequence of the specimen surface temperature and the original data document with binary format.
The original file is a string of data in the way of line by line, frame by frame.
Using MATLAB, the data can be read easily at any point, in any row, column and frame.
Then the data was stored into a three-dimensional matrix (240 × 320 × N). 240, 320, N respectively means rows, columns and frames.
The data is extracted using MATLAB, surface temperature vs. time plot over the fist hole and over a sound area is shown in Fig. 4.

This paper introduces three cases, briefly analyzes the features of ancient buildings, their difference with other cultural relics, the special requirements of unmanned helicopter survey, and follow-up data processing methods.
Aerial photographs and ground-taken photographs were processed with a Photomodeler software to extract spatial data of feature points, on the basis of which the elevation and floor plan were made manually (Figure5).
Laser scanning is easier to learn than Photogrammetry, but it cannot obtain complete survey data due to its ground-based limitations.
Data are to be processed and drawings are to be done manually.
Simple and having beautiful lines Either to generate dense point clouds to facilitate drawing or manually obtain 3D data of feature points for orthographic projection drawings.

Library Management System The Subsystem For Handprint Database The Subsystem For Collecting Information Diving And Picking Subsystem Self-service Subsystem For Borrowing And Returning Books Data Bank For Books System Library Maintain System Fig.1 Library management system 3.2 The subsystem for handprint database This system stores for all handprint system which is registered by users.
RFID reader-writer reads each floor RFID data, sends collected data to control main engine, update and show the current data information[10]. 3.4 Diving and picking subsystem The main parts is intelligent machine for diving and picking books.
It basis books’ electronic label, and according to given class to automatic distinguish and return books. 3.5 Self-service subsystem for borrowing and returning books The users handle borrowing and returning procedures in library self-service machine and providing handprint to accomplish register. 3.6 Data bank for books system This system stores all abstract information E-journal for books in library, and provides back-office support for looking for books. 3.7 Library maintain system This system accomplish normal function for monitoring and preserving of library.
A new method for data redundancy reduction in RFID middleware[C].//2010 5th International Symposium on Telecommunications.2010:175-180

The velocity and density data were found to be accurate with in ± 0.10%and ± 0.01% respectively.
Adiabatic Compressibility The adiabatic compressibility of the mannose + water + NaCl, KCl, MgCl2and CaCl2 were determined at 303 K from the density and velocity data.
Such a decrease in adiabatic compressibility observed in the ternary systems (mannose + water + electrolytes)clearly confirms the conclusion drawn from the ultrasonic velocity data.
The фv values increase due to reduction in the electrostriction effect at terminals, whereas it decreases due to disruption of side group hydration by that of the charged end.
The partial molal volume at infinite dilution фov was calculated by taking an average data points.

Using the initial data massive it was defined that there were 2546.28 t (92.1%) of hot rolled strip rejected to second grade due to defect “non-flatness” with thickness up to 3.9mm, 218.7 t (7.9%) of strip with thickness of 4÷12mm.
Using the initial data massive it was defined that there were 16247.33t (15%) hot rolled strip produced with width up to 1100mm, 32700.75t (30.26%) with width of 1200÷1300mm and 59088.71t (54.7%) with width of 1400÷1500mm.
For that purpose there were data on profiling of work rolls in finishing stands treated by methods of mathematical statistics (Table 1).
Sampled data were checked for presence of gross errors as per Student criterion.
As a result of regression analysis of initial data massive there were following response functions received for forecasting of metal volume, rolled with the defect “non-flatness” with definite cumulative profiling of work rolls.